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Welcome to the UC Irvine Center for Cognitive Neuroscience Blog. Here you will find information on current and upcoming events as well as news about faculty, students, and research from the Center. For more information about who we are and what we do, please visit our website: www.ccns.uci.edu

Tuesday, October 30, 2007

AbstractAs the clinical and cognitive neurosciences mature, multivariate analysis techniques for neuroimaging data have received increasing attention since they possess some attractive features that cannot be easily realized by the more commonly used univariate, voxel-wise, techniques: (1) multivariate approaches evaluate correlation/covariance of activation across brain regions, rather than proceeding on a voxel-wise basis. Thus, their results can be more easily interpreted as a signature of neural networks. (2) Multivariate techniques also lend themselves better to prospective application of results obtained from the analysis of one dataset to entirely new datasets. Multivariate techniques are thus well placed to provide information about mean differences and correlations with behavior, similarly to univariate approaches, but with potentially greater statistical power and better reproducibility checks. Despite these attractive features, the barrier of entry to the use of multivariate approaches has been high, preventing more widespread application in the community. We have therefore proposed a series of studies comparing multivariate approaches amongst each other and with traditional univariate approaches in didactic reports and comprehensive review papers, using simulated as well as real-world data sets.

In my presentation, I will give a simple mathematical overview of univariate and multivariate approaches. Then I‚ll present two examples of multivariate analysis applied to: (1) an fMRI data set from a study using a delayed-response task, and (2) a clinical data set from a study comparing healthy elderly participants with early Alzheimer‚s disease patients, using resting scans of regional Cerebral Blood Flow. The presentation will close with remaining challenges and directions for the future.

Brewer is a visual neuroscientist who focuses on the use of brain imaging and behavioral research to gain a better understanding of the organization and functions of the brain related to eyesight. Her research has been widely published in multiple peer-reviewed journals.

Brewer attended Stanford University where she received a B.A in comparative literature, a B.S. in biological science, a Ph.D. in neuroscience, and most recently, an M.D. from the School of Medicine at Stanford. She has spent the last two years working as a postdoctoral research associate at Stanford while finishing her M.D.

Jeffrey Krichmar Assistant Professor, Cognitive Sciences

Krichmar is a computational neuroscientist whose research interests include biologically plausible models of learning and memory, the effect of neural architecture on neural function, and testing theories of the nervous system with brain-based devices that interact with the environment. Specifically, his work includes the building of robots with simulated nervous systems in order to test theoretical models of brain functions.

He received a B.S. in computer science from the University of Massachusetts at Amherst, an M.S. in computer science from George Washington University, and a Ph.D. in computational sciences and informatics from George Mason University, after which he spent 15 years as a software engineer on projects ranging from the PATRIOT Missile System at the Raytheon Corporation to Air Traffic Control for the Federal Systems Division of IBM. He later became an assistant professor at the Krasnow Institute for Advanced Study at George Mason University. Most recently, he was a senior research fellow in Theoretical Neurobiology at the Neurosciences Institute in San Diego.